38892 “cells”/barcodes as filtered by Cell Ranger
Hs_GA2123_Trachea_v3@raw.data@Dim
[1] 22084 38892
head(Hs_GA2123_Trachea_v3@cell.names)
Hs_GA2123_Trachea_v3@data@Dim
[1] 22084 9693
cell_name<-read.table(text=Hs_GA2123_Trachea_v3@cell.names,sep="_",colClasses = "character")
age<-cell_name[,1]
names(age)<-Hs_GA2123_Trachea_v3@cell.names
Hs_GA2123_Trachea_v3<-AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = age, col.name = "age")
table(Hs_GA2123_Trachea_v3@meta.data$age)
GA21wk GA23wk
7623 2070
ribo.genes <- grep(pattern = "^RP[SL][[:digit:]]", x = rownames(x = Hs_GA2123_Trachea_v3@data), value = TRUE)
percent.ribo <- Matrix::colSums(Hs_GA2123_Trachea_v3@raw.data[ribo.genes, ])/Matrix::colSums(Hs_GA2123_Trachea_v3@raw.data)
Hs_GA2123_Trachea_v3 <- AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = percent.ribo, col.name = "percent.ribo")
Hs_GA2123_Trachea_v3 <- NormalizeData(object = Hs_GA2123_Trachea_v3)
Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Hs_GA2123_Trachea_v3 <- ScaleData(object = Hs_GA2123_Trachea_v3)
Scaling data matrix
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Hs_GA2123_Trachea_v3 <- FindVariableGenes(object = Hs_GA2123_Trachea_v3, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)
Calculating gene means
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Hs_GA2123_Trachea_v3 <- RunPCA(object = Hs_GA2123_Trachea_v3, do.print = FALSE)
Hs_GA2123_Trachea_v3 <- ProjectPCA(object = Hs_GA2123_Trachea_v3, do.print = FALSE)
PCHeatmap(object = Hs_GA2123_Trachea_v3, pc.use = 1:10, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)

PCElbowPlot(object = Hs_GA2123_Trachea_v3)

n.pcs = 20
res.used <- 0.8
Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)
Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2)

TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = F,group.by="age",pt.size = 0.1)

n.pcs = 20
res.used <- 1.0
Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE,force.recalc=T)
Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.1")


Hs_GA2123_Trachea_v3<-SetAllIdent(Hs_GA2123_Trachea_v3,id="res.1")
GA2123wk_v3.res1.clust.markers <- FindAllMarkers(object = Hs_GA2123_Trachea_v3, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
GA2123wk_v3.res1.clust.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)
write.table(GA2123wk_v3.res1.clust.markers,"GA2123wk_v3.res1.markers.txt",sep="\t")
Hs_v3_res1_8_18<-FindMarkers(Hs_GA2123_Trachea_v3,ident.1=c(8),ident.2=c(18),only.pos = F)
| | 0 % ~calculating
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 04s
Hs_v3_res1_8_18
Hs_v3_res1_2over1<-FindMarkers(Hs_GA2123_Trachea_v3,ident.1=c(2),ident.2=c(1),only.pos = T)
| | 0 % ~calculating
|+ | 2 % ~03s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 03s
Hs_v3_res1_2over1
Hs_v3_res1_21_20<-FindMarkers(Hs_GA2123_Trachea_v3,ident.1=c(21),ident.2=c(20),only.pos = T)
| | 0 % ~calculating
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Hs_v3_res1_21_20
n.pcs = 20
res.used <- 1.2
Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)
Build parameters exactly match those of already computed and stored SNN. To force recalculation, set force.recalc to TRUE.
Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.1.2")

n.pcs = 20
res.used <- 1.4
Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)
Build parameters exactly match those of already computed and stored SNN. To force recalculation, set force.recalc to TRUE.
Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.1.4")

sum(is.na(Hs_GA2123_Trachea_v3@meta.data$doublet_score))
[1] 0
Hs_GA2123_Trachea_v3<-SetAllIdent(Hs_GA2123_Trachea_v3,id="age")
VlnPlot(object = Hs_GA2123_Trachea_v3, features.plot = c("doublet_score"), nCol = 1,group.by="res.1.4",point.size.use=0.3,ident.include = "GA21wk")



VlnPlot(object = Hs_GA2123_Trachea_v3, features.plot = c("SOX10","PHOX2A", "PHOX2B","CHGA","ASCL1","RET"), nCol = 1,group.by="res.1.4",point.size.use=0.3)
subset the Non-EPCAM cells:
Hs_GA2123_Trachea_v3 <- SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "res.1.4")
Hs_GA2123_Trachea_v3_nonEpcam<-SubsetData(object=Hs_GA2123_Trachea_v3,ident.use=c(0:7,9:13,15,16,18,20:24))
table(Hs_GA2123_Trachea_v3_nonEpcam@meta.data$res.1.4)
0 1 10 11 12 13 15 16 18 2 20 21 22 23 24 3 4 5 6 7 9
937 862 405 401 370 351 316 290 248 825 189 121 90 88 65 550 481 476 461 447 414
colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data)[colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data) == 'res.0.8'] <- 'orig.0.8'
colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data)[colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data) == 'res.1.4'] <- 'orig.1.4'
colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data)[colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data) == 'res.1.2'] <- 'orig.1.2'
Hs_GA2123_Trachea_v3_nonEpcam <- ScaleData(object = Hs_GA2123_Trachea_v3_nonEpcam)
Scaling data matrix
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Hs_GA2123_Trachea_v3_nonEpcam <- FindVariableGenes(object = Hs_GA2123_Trachea_v3_nonEpcam, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)
Calculating gene means
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

run PCA on the set of genes
Hs_GA2123_Trachea_v3_nonEpcam <- RunPCA(object = Hs_GA2123_Trachea_v3_nonEpcam, do.print = FALSE)
#PCAPlot(Hs_GA2123_Trachea_v3_nonEpcam)
Hs_GA2123_Trachea_v3_nonEpcam <- ProjectPCA(object = Hs_GA2123_Trachea_v3_nonEpcam, do.print = F)
PCElbowPlot(object = Hs_GA2123_Trachea_v3_nonEpcam)

PCHeatmap(object = Hs_GA2123_Trachea_v3_nonEpcam, pc.use = 1:20, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)

n.pcs = 20
res.used <- 0.8
Hs_GA2123_Trachea_v3_nonEpcam <- FindClusters(object = Hs_GA2123_Trachea_v3_nonEpcam, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)
Build parameters exactly match those of already computed and stored SNN. To force recalculation, set force.recalc to TRUE.Clustering parameters for resolution 0.8 exactly match those of already computed.
To force recalculation, set force.recalc to TRUE.
Hs_GA2123_Trachea_v3_nonEpcam <- RunTSNE(object = Hs_GA2123_Trachea_v3_nonEpcam, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = Hs_GA2123_Trachea_v3_nonEpcam, do.label = T,group.by="res.0.8")


table(Hs_GA2123_Trachea_v3_nonEpcam@meta.data$orig.1.4,Hs_GA2123_Trachea_v3_nonEpcam@meta.data$res.0.8)
0 1 10 11 12 13 14 15 16 17 2 3 4 5 6 7 8 9
0 917 10 0 0 0 1 1 0 0 0 0 4 4 0 0 0 0 0
1 13 739 65 18 5 10 0 0 0 0 0 11 0 0 0 1 0 0
10 2 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 401
11 0 0 0 1 1 0 0 1 0 0 396 0 0 0 1 1 0 0
12 0 9 0 313 47 0 0 0 0 0 0 0 0 0 0 1 0 0
13 11 5 1 0 3 0 0 0 0 0 0 0 329 1 0 1 0 0
15 0 22 274 0 3 0 0 0 0 0 0 16 0 0 0 1 0 0
16 0 9 5 4 256 0 1 0 0 0 0 0 0 0 13 0 2 0
18 1 17 1 0 0 227 0 1 0 0 0 0 0 0 0 0 0 1
2 15 37 30 0 0 0 0 0 0 0 0 743 0 0 0 0 0 0
20 0 0 0 0 0 0 0 0 0 0 0 0 4 184 0 0 1 0
21 5 0 0 0 0 0 115 0 0 0 0 0 0 0 0 1 0 0
22 0 0 0 0 0 0 0 1 85 0 0 0 0 0 4 0 0 0
23 0 0 0 0 0 0 0 88 0 0 0 0 0 0 0 0 0 0
24 0 0 0 0 0 0 0 0 0 65 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 1 0 0 0 0 0 542 7 0 0
4 5 3 1 0 0 0 0 0 0 0 0 4 0 3 0 463 1 1
5 1 4 1 1 2 1 0 3 5 0 0 1 1 0 6 1 449 0
6 0 0 0 0 0 0 0 0 0 0 0 0 4 457 0 0 0 0
7 19 0 0 0 0 0 0 0 0 0 0 0 414 12 0 1 1 0
9 0 0 0 0 0 0 0 0 0 0 414 0 0 0 0 0 0 0
library(ggalluvial)
ggplot(data=Hs_GA2123_Trachea_v3_nonEpcam@meta.data,aes(axis1=orig.1.4,axis2=res.0.8))+geom_alluvium(aes(fill=res.0.8))+geom_stratum(width = 1/12, fill = "black", color = "grey") +geom_label(stat = "stratum", label.strata = TRUE)+scale_x_discrete(limits = c("orig.1.4", "res.0.8"), expand = c(.05, .05))

Now subset the basal, ciliated, and secretory:
Hs_GA2123_Trachea_v3 <- SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "res.1.4")
Hs_GA2123_Trachea_v3_sub1<-SubsetData(object=Hs_GA2123_Trachea_v3,ident.use=c(8,14,19))
table(Hs_GA2123_Trachea_v3_sub1@meta.data$res.1.4)
colnames(Hs_GA2123_Trachea_v3_sub1@meta.data)[colnames(Hs_GA2123_Trachea_v3_sub1@meta.data) == 'res.0.8'] <- 'orig.0.8'
colnames(Hs_GA2123_Trachea_v3_sub1@meta.data)[colnames(Hs_GA2123_Trachea_v3_sub1@meta.data) == 'res.1.4'] <- 'orig.1.4'
colnames(Hs_GA2123_Trachea_v3_sub1@meta.data)[colnames(Hs_GA2123_Trachea_v3_sub1@meta.data) == 'res.1.2'] <- 'orig.1.2'
Hs_GA2123_Trachea_v3_sub1 <- ScaleData(object = Hs_GA2123_Trachea_v3_sub1)
Scaling data matrix
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Hs_GA2123_Trachea_v3_sub1 <- FindVariableGenes(object = Hs_GA2123_Trachea_v3_sub1, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)
Calculating gene means
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Calculating gene variance to mean ratios
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run PCA on the set of genes
Hs_GA2123_Trachea_v3_sub1 <- RunPCA(object = Hs_GA2123_Trachea_v3_sub1, do.print = FALSE)
#PCAPlot(Hs_GA2123_Trachea_v3_sub1)
Hs_GA2123_Trachea_v3_sub1 <- ProjectPCA(object = Hs_GA2123_Trachea_v3_sub1, do.print = F)
PCElbowPlot(object = Hs_GA2123_Trachea_v3_sub1)

PCHeatmap(object = Hs_GA2123_Trachea_v3_sub1, pc.use = 1:12, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)

n.pcs = 16
res.used <- 0.8
Hs_GA2123_Trachea_v3_sub1 <- FindClusters(object = Hs_GA2123_Trachea_v3_sub1, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)
Hs_GA2123_Trachea_v3_sub1 <- RunTSNE(object = Hs_GA2123_Trachea_v3_sub1, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = Hs_GA2123_Trachea_v3_sub1, do.label = T)



prop.table(table(Hs_GA2123_Trachea_v3_sub1@meta.data$age,Hs_GA2123_Trachea_v3_sub1@meta.data$res.0.8),1)
0 1 2 3 4 5 6 7
GA21wk 0.18484848 0.20303030 0.08333333 0.16818182 0.19090909 0.06363636 0.06060606 0.04545455
GA23wk 0.18333333 0.10000000 0.31111111 0.10833333 0.05277778 0.12500000 0.06388889 0.05555556
n.pcs = 16
res.used <- 1.2
Hs_GA2123_Trachea_v3_sub1 <- FindClusters(object = Hs_GA2123_Trachea_v3_sub1, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)
Build parameters exactly match those of already computed and stored SNN. To force recalculation, set force.recalc to TRUE.
Hs_GA2123_Trachea_v3_sub1 <- RunTSNE(object = Hs_GA2123_Trachea_v3_sub1, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)


Hs_v3_sub1_res1.2_c8over2_4<-FindMarkers(Hs_GA2123_Trachea_v3_sub1,ident.1=c(8),ident.2 = c(2,4),only.pos = TRUE)
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Hs_v3_sub1_res1.2_c8over2_4
library(plyr)
Hs_GA2123_Trachea_v3_sub1@meta.data$cell_type<-mapvalues(Hs_GA2123_Trachea_v3_sub1@meta.data$res.1.2,from=c("0","1","2","3","4","5","6","7","8"),to=c("Secretory_SMG","Ciliated","Basal_SE","Epcam_ECM","Basal_SE","Myoepithelial","Ciliated_Foxn4","Secretory_SE","Basal_SMG"))

to annotate Hs_GA2123_Trachea_v3
Hs_v3_type_sub1<-Hs_GA2123_Trachea_v3_sub1@meta.data$cell_type
names(Hs_v3_type_sub1)<-Hs_GA2123_Trachea_v3_sub1@cell.names
Hs_GA2123_Trachea_v3@meta.data$cell_type<-mapvalues(Hs_GA2123_Trachea_v3@meta.data$res.1,from=c("0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22"),to=c("Fibroblast","Fibroblast","Fibroblast","VascularEndothelial","Fibroblast","Fibroblast","CyclingFibroblast","Fibroblast","Chondrocyte","Basal","Schwann/Neural","Fibroblast","Secretory","MesenchymalProgenitor","Stem","Fibroblast","Ciliated","Fibroblast","Chondrocyte","Immune","Muscle","Muscle","LymphaticEndothelial"))
Hs_GA2123_Trachea_v3<-AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = Hs_v3_type_sub1, col.name = "specific_type")
table(Hs_GA2123_Trachea_v3@meta.data$specific_type)
Basal_SE Basal_SMG Ciliated Ciliated_Foxn4 Epcam_ECM Myoepithelial Secretory_SE Secretory_SMG
308 30 169 66 150 67 54 176
Hs_GA2123_Trachea_v3@meta.data$specific_type <- ifelse(is.na(Hs_GA2123_Trachea_v3@meta.data$specific_type), as.character(Hs_GA2123_Trachea_v3@meta.data$cell_type), as.character(Hs_GA2123_Trachea_v3@meta.data$specific_type))
now we have annotation for all cells:
table(Hs_GA2123_Trachea_v3@meta.data$specific_type)
Basal_SE Basal_SMG Chondrocyte Ciliated Ciliated_Foxn4 CyclingFibroblast
308 30 650 169 66 479
Epcam_ECM Fibroblast Immune LymphaticEndothelial MesenchymalProgenitor Muscle
150 5359 121 65 316 177
Myoepithelial Schwann/Neural Secretory_SE Secretory_SMG Stem VascularEndothelial
67 405 54 176 286 815

print(levels(Hs_GA2123_Trachea_v3@ident))
[1] "Basal_SE" "Basal_SMG" "Chondrocyte" "Ciliated" "Ciliated_Foxn4"
[6] "CyclingFibroblast" "Epcam_ECM" "Fibroblast" "Immune" "LymphaticEndothelial"
[11] "MesenchymalProgenitor" "Muscle" "Myoepithelial" "Schwann/Neural" "Secretory_SE"
[16] "Secretory_SMG" "Stem" "VascularEndothelial"
Hs_GA2123_Trachea_v3<-SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "specific_type")
Hs_GA2123_Trachea_v3@ident = factor(Hs_GA2123_Trachea_v3@ident,levels(Hs_GA2123_Trachea_v3@ident)[c(1,15,5,4,2,16,13,7,17,14,9,10,18,12,3,11,8,6)])


df_Hs<-FetchData(Hs_GA2123_Trachea_v3,c("ANO1","CFTR","SERPINB3","MUC16","specific_type"))

load(file="seurat_GA2123wk_v3.RData")